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2022 IEEE 11th Data Driven Control and Learning Systems Conference (DDCLS)最新文献

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Iterative Learning Control for Multi-Agent Systems Over Unknown Fading Networks 未知衰落网络中多智能体系统的迭代学习控制
2022 IEEE 11th Data Driven Control and Learning Systems Conference (DDCLS) Pub Date : 2022-08-03 DOI: 10.1109/DDCLS55054.2022.9858582
Xun He, Hao Jiang, D. Shen
{"title":"Iterative Learning Control for Multi-Agent Systems Over Unknown Fading Networks","authors":"Xun He, Hao Jiang, D. Shen","doi":"10.1109/DDCLS55054.2022.9858582","DOIUrl":"https://doi.org/10.1109/DDCLS55054.2022.9858582","url":null,"abstract":"This paper studies the consensus problem for multi-agent systems over unknown fading networks. The directed graph is used to describe the communication between agents. The fading process is modeled by a random variable multiplied to the original signal. It is the first time to study the effect of unknown fading networks in a distributed structure. An iterative estimation is presented to obtain the statistical property of fading networks. Moreover, a distributed learning control scheme is proposed to establish the learning consensus performance. The input errors for all agents are strictly proved convergent to zero in the mean-square sense. In the end, numerical simulations are presented to verify the proposed scheme.","PeriodicalId":412672,"journal":{"name":"2022 IEEE 11th Data Driven Control and Learning Systems Conference (DDCLS)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114392119","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 1
Data-Driven Norm-Optimal-Gain-Adaptive Iterative Learning Control for LDTI Systems LDTI系统的数据驱动范数最优增益自适应迭代学习控制
2022 IEEE 11th Data Driven Control and Learning Systems Conference (DDCLS) Pub Date : 2022-08-03 DOI: 10.1109/DDCLS55054.2022.9858475
Chuyang Liu, Xiaoe Ruan
{"title":"Data-Driven Norm-Optimal-Gain-Adaptive Iterative Learning Control for LDTI Systems","authors":"Chuyang Liu, Xiaoe Ruan","doi":"10.1109/DDCLS55054.2022.9858475","DOIUrl":"https://doi.org/10.1109/DDCLS55054.2022.9858475","url":null,"abstract":"For a class of repeatable linear discrete time-invariant systems, the paper constructs a data-driven norm-optimal-gain-adaptive iterative learning control scheme. The scheme is sequentially processed in an interactive mode. The estimation of the system Markov parameters is updated by minimizing the discrepancy between the estimated system output and the real output, utilizing system input and output data from previous iterations, thereby omitting the time-consuming system identification process. Simultaneously, the estimated Markov parameters are adopted for formulating the learning-gain vector of iterative learning control in the light of minimizing the weighted sum of the quadratic norm of the learning compensation vector and the tracking-error vector. Numerical simulations reveal the feasibility of the proposed scheme to achieve trajectory tracking while declining the estimated error of system Markov parameters.","PeriodicalId":412672,"journal":{"name":"2022 IEEE 11th Data Driven Control and Learning Systems Conference (DDCLS)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128261529","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Low-Dimensional Non-Linear Luenberger Observer for Thermal Distribution of Wafer in Rapid Thermal Processing Process 快速热加工过程中硅片热分布的低维非线性Luenberger观测器
2022 IEEE 11th Data Driven Control and Learning Systems Conference (DDCLS) Pub Date : 2022-08-03 DOI: 10.1109/DDCLS55054.2022.9858482
T. Xiao, Ying Kong, Xiao-dong Li
{"title":"Low-Dimensional Non-Linear Luenberger Observer for Thermal Distribution of Wafer in Rapid Thermal Processing Process","authors":"T. Xiao, Ying Kong, Xiao-dong Li","doi":"10.1109/DDCLS55054.2022.9858482","DOIUrl":"https://doi.org/10.1109/DDCLS55054.2022.9858482","url":null,"abstract":"A low-dimensional non-linear observer is developed in this paper for the estimation of the thermal distribution in a rapid thermal processing (RTP) process. Based on the reduced model obtained from Galerkin's method, a low-dimensional non-linear Luenberger observer is designed to re-construct the thermal distribution from the output data of the point temperature sensors. Test results in a RTP system show fast convergence speed and small estimated errors of the proposed observer.","PeriodicalId":412672,"journal":{"name":"2022 IEEE 11th Data Driven Control and Learning Systems Conference (DDCLS)","volume":"117 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134383416","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Wavelet Function Based Spectral Model Calibration for Measuring Crystallization Solution via ATR-FTIR Spectroscopy 基于小波函数的ATR-FTIR光谱测量结晶溶液光谱模型标定
2022 IEEE 11th Data Driven Control and Learning Systems Conference (DDCLS) Pub Date : 2022-08-03 DOI: 10.1109/DDCLS55054.2022.9858585
Xiaojing Pei, Tao Liu, Jingxiang Liu, Shoulin Hao, Siwei Yang
{"title":"Wavelet Function Based Spectral Model Calibration for Measuring Crystallization Solution via ATR-FTIR Spectroscopy","authors":"Xiaojing Pei, Tao Liu, Jingxiang Liu, Shoulin Hao, Siwei Yang","doi":"10.1109/DDCLS55054.2022.9858585","DOIUrl":"https://doi.org/10.1109/DDCLS55054.2022.9858585","url":null,"abstract":"For measuring the solution concentration of crystallization process by ATR-FTIR spectroscopy, this paper proposes an improved spectral model calibration method to guarantee in-situ measurement accuracy, based on wavelet function regression. The spectral model structure is composed of wavelet basis functions, which could address the nonlinear properties and high-dimensional problem of infrared spectral variables. Moreover, the mean centralization strategy is taken to reduce the influence from measurement noise. In addition, a comparative study on measuring the solution concentration of the L-glutamic acid (LGA) crystallization process is conducted in terms of using different modeling methods and spectral variables. It is found that the wavelet function regression with all spectral variables could obtain good measurement accuracy compared to the traditional partial least-squares (PLS) method. Experimental results well demonstrate the measurement accuracy of the proposed spectral calibration method.","PeriodicalId":412672,"journal":{"name":"2022 IEEE 11th Data Driven Control and Learning Systems Conference (DDCLS)","volume":"108 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134643764","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 1
Operating Condition Identification of Complete Wind Turbine using DBN and Improved DDPG-SOM 基于DBN和改进DDPG-SOM的全机运行状态识别
2022 IEEE 11th Data Driven Control and Learning Systems Conference (DDCLS) Pub Date : 2022-08-03 DOI: 10.1109/DDCLS55054.2022.9858494
Zheng Wang, Xuening Chu
{"title":"Operating Condition Identification of Complete Wind Turbine using DBN and Improved DDPG-SOM","authors":"Zheng Wang, Xuening Chu","doi":"10.1109/DDCLS55054.2022.9858494","DOIUrl":"https://doi.org/10.1109/DDCLS55054.2022.9858494","url":null,"abstract":"Although there have been a lot of researches on identifying the condition of components in wind turbine, such as blade, gearbox, bearings, etc., maintenance immediately after some component faults is not an optimal choice in practical use, since it leads to high-frequency downtime and large unnecessary cost. Therefore, the condition identification of complete wind turbine is required. In this paper, we propose a novel method based on data-driven techniques and information fusion model. Firstly, a DBN is used for capturing information about the condition of components in wind turbine. Then, a new information fusion model is presented for integrating the information about conditions of components based on an improved DDPG by incorporating a random module and SOM. Based on the output of the improved DDPG-SOM, a status index is calculated representing the condition of the whole machine. A case study based on real SCADA data is conducted to show the effectiveness and superiority of the proposed method.","PeriodicalId":412672,"journal":{"name":"2022 IEEE 11th Data Driven Control and Learning Systems Conference (DDCLS)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133102753","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Particle Filter Based Robust State and Parameter Estimation for Estimating SOC and Discharge Current of Lithium Batteries 基于粒子滤波的锂电池荷电状态和放电电流估计
2022 IEEE 11th Data Driven Control and Learning Systems Conference (DDCLS) Pub Date : 2022-08-03 DOI: 10.1109/DDCLS55054.2022.9858503
Yijia He, Zhihui Hong, Zhengjiang Zhang, Shipei Huang
{"title":"Particle Filter Based Robust State and Parameter Estimation for Estimating SOC and Discharge Current of Lithium Batteries","authors":"Yijia He, Zhihui Hong, Zhengjiang Zhang, Shipei Huang","doi":"10.1109/DDCLS55054.2022.9858503","DOIUrl":"https://doi.org/10.1109/DDCLS55054.2022.9858503","url":null,"abstract":"The State of Charge (SOC) of lithium batteries can be estimated effectively by particle Filter (PF). However, it is difficult to accurately obtain the value of the parameter discharge current of the lithium battery in the actual process, which affects the accuracy of the PF for SOC estimation. In order to solve the problem, this paper proposes a particle filter based robust state and parameter estimation (PF-RSPE) method for estimating SOC and discharge current of lithium batteries. The method estimates SOC and the unknown parameter discharge current simultaneously. The unknown parameter is expanded to the states of the model. The kernel smoothing method with the fitness factor β is applied in the procedures of the simultaneous estimation, which improves the performance of the estimation. Finally, the proposed method is applied to the equivalent model of Thevenin battery for simulation verification. The results show that when the parameter discharge current is unknown, the PF-RSPE method can effectively estimate both SOC and discharge current.","PeriodicalId":412672,"journal":{"name":"2022 IEEE 11th Data Driven Control and Learning Systems Conference (DDCLS)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127068536","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Deep Learning of Process Data with Supervised Variational Auto-encoder for Soft Sensor 基于监督变分自编码器的软测量过程数据深度学习
2022 IEEE 11th Data Driven Control and Learning Systems Conference (DDCLS) Pub Date : 2022-08-03 DOI: 10.1109/DDCLS55054.2022.9858451
Xiaochu Tang, Jiawei Yan, Zhihuan Song, Xinmin Zhang
{"title":"Deep Learning of Process Data with Supervised Variational Auto-encoder for Soft Sensor","authors":"Xiaochu Tang, Jiawei Yan, Zhihuan Song, Xinmin Zhang","doi":"10.1109/DDCLS55054.2022.9858451","DOIUrl":"https://doi.org/10.1109/DDCLS55054.2022.9858451","url":null,"abstract":"Nonlinearity and uncertainty are two critical characteristics when process data modeling is applied to soft sensor. In this paper, a supervised variational auto-encoder (SVAE) is developed to capture both nonlinear and untcertain feature for regression modeling. SVAE, as a deep generative model, provides a probabilistic framework, based on which the deep nonlinear feature extraction is carried out and a probabilistic respresentation can be obtained. In this way, the probability distribution mapping between process data and key quality variables is learned so that quality prediction can be well achieved. The feasibility of the proposed method is illustrated by a numerical example and an industrial example, and the effectiveness of the proposed model is verified by comparing with the linear model.","PeriodicalId":412672,"journal":{"name":"2022 IEEE 11th Data Driven Control and Learning Systems Conference (DDCLS)","volume":"192 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129240647","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 1
An Adaptive Integral Sliding Mode Control with Allocation Scheme for Reconfigurable Flight Array 一种可重构飞行阵列自适应积分滑模分配控制方法
2022 IEEE 11th Data Driven Control and Learning Systems Conference (DDCLS) Pub Date : 2022-08-03 DOI: 10.1109/DDCLS55054.2022.9858401
Jianquan Yang, Chunxi Yang, J. Na, Yiming Li, Yashan Xing
{"title":"An Adaptive Integral Sliding Mode Control with Allocation Scheme for Reconfigurable Flight Array","authors":"Jianquan Yang, Chunxi Yang, J. Na, Yiming Li, Yashan Xing","doi":"10.1109/DDCLS55054.2022.9858401","DOIUrl":"https://doi.org/10.1109/DDCLS55054.2022.9858401","url":null,"abstract":"This paper proposes a tracking control strategy for a reconfigurable flight array system with varible configuration. The presented control scheme does not dependent on the accurate mathematical model of system. By employing a pseudo-inverse method with the control efficiency matrix, an control allocation method is constructed to achieve trajectory tracking for a multi-configuration flight array system. To maintain the trajectory tracking performance, an adaptive integral sliding mode controller is designed by using the distance information of the sliding variables and the boundary layer. Theoretical analysis is studied in terms of the Lyapunov theory. Finally, comparative simulation results are provided to demonstrate the effectiveness of the proposed method.","PeriodicalId":412672,"journal":{"name":"2022 IEEE 11th Data Driven Control and Learning Systems Conference (DDCLS)","volume":"90 ","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120975185","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Identification of Cogging Force in Ironed Linear Motor Based on RBF Neural Networks using Hybrid Self-Adaptive TLBO 基于混合自适应TLBO的RBF神经网络的熨烫直线电机齿槽力辨识
2022 IEEE 11th Data Driven Control and Learning Systems Conference (DDCLS) Pub Date : 2022-08-03 DOI: 10.1109/DDCLS55054.2022.9858489
Siwen Chen, Yang Liu, Fazhi Song
{"title":"Identification of Cogging Force in Ironed Linear Motor Based on RBF Neural Networks using Hybrid Self-Adaptive TLBO","authors":"Siwen Chen, Yang Liu, Fazhi Song","doi":"10.1109/DDCLS55054.2022.9858489","DOIUrl":"https://doi.org/10.1109/DDCLS55054.2022.9858489","url":null,"abstract":"The cogging force is an intrinsic characteristic of linear motor, which is caused by the structure of linear motor. As a crucial disturbance, the cogging force seriously impairs the positioning accuracy of linear motor. The existing compensation approaches are hardly implemented or have a low accuracy in practice, due to the inaccurate model of the cogging force. In this paper, the identification problem of the cogging force of the linear motor is investigated. To compensate the unknown nonlinearity, the RBF neural network is utilized for fitting. Moreover, to overcome the local optimal solutions involved with the traditional TLBO method, a hybrid self-adaptive TLBO algorithm is adopted to train the neural network. Finally, experimental results confirm the effectiveness and advantage of the proposed method.","PeriodicalId":412672,"journal":{"name":"2022 IEEE 11th Data Driven Control and Learning Systems Conference (DDCLS)","volume":"73 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124448141","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Energy Balance Based Attack Detection for Cyber Physical Systems 基于能量平衡的网络物理系统攻击检测
2022 IEEE 11th Data Driven Control and Learning Systems Conference (DDCLS) Pub Date : 2022-08-03 DOI: 10.1109/DDCLS55054.2022.9858499
Zhuyuan Li, Ying Yang, Zhengen Zhao, Ruijie Liu
{"title":"Energy Balance Based Attack Detection for Cyber Physical Systems","authors":"Zhuyuan Li, Ying Yang, Zhengen Zhao, Ruijie Liu","doi":"10.1109/DDCLS55054.2022.9858499","DOIUrl":"https://doi.org/10.1109/DDCLS55054.2022.9858499","url":null,"abstract":"In this article, the deficiency of a widely-used observer-based attack detection scheme for cyber physical systems (CPS) is analyzed and then a novel dissipativity-based attack detector is proposed. Firstly, the definition of kernel attacks is introduced to capture the stealthy attacks which can not be detected through well-established observer-based techniques. Further, a data-driven design of kernel attacks based on subspace identification methods deploying input and output data is presented. To detect kernel attacks, a dissipativity-based attack detector is formulated in light of energy balance equation, providing a complementary strategy to guarantee reliable detection of attacks. Finally, the validity of the presented approach is demonstrated in a numerical example.","PeriodicalId":412672,"journal":{"name":"2022 IEEE 11th Data Driven Control and Learning Systems Conference (DDCLS)","volume":"153 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123316367","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
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